Depth estimation in light field structured light using SROACC-Net

Changheng Fan; Yuhang Wang; Kai Sun; Xinyi Cui; Zhizhi Zhang; XinJun Zhu

doi:10.1117/12.3045269

9 October 2024 Depth estimation in light field structured light using SROACC-Net

Changheng Fan, Yuhang Wang, Kai Sun, Xinyi Cui, Zhizhi Zhang, XinJun Zhu

Proceedings Volume 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024); 132880D (2024) https://doi.org/10.1117/12.3045269
Event: Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 2024, Chengdu, China

Abstract

Depth or disparity estimation plays an important part in computer graphics and computer vision in recent years. Light field imaging has been widely used in the field of depth or disparity estimation because it contains information on light direction and intensity which can provide dense depth estimation. This paper proposes the SROACC-Net for light field structured light disparity estimation based on the OACC-Net with occlusion-aware cost constructor, where squeeze-andexcitation residual net (SE-ResNet) module is added to improve the accuracy. Moreover, Huber-SSIM loss function is designed to boost the performance of the model. The experimental results demonstrate that the SROACC-Net outperforms the OACC-Net in light field structured light depth prediction. The SROACC-Net under light field structured light provides a promising way for depth estimation in computer graphics and computer vision.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Changheng Fan, Yuhang Wang, Kai Sun, Xinyi Cui, Zhizhi Zhang, and XinJun Zhu "Depth estimation in light field structured light using SROACC-Net", Proc. SPIE 13288, Fourth International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2024), 132880D (9 October 2024); https://doi.org/10.1117/12.3045269

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available